Method of evaluation of a bit error rate measurement for indication of a channel quality

ABSTRACT

In a method for efficient evaluation of measurement values from a bit error rate measurement for indication of channel quality, the characteristic of the transmission channel, and the bit error rate which is dependent on it, are taken into account via their stochastic distribution. This results in the bit error rate being quantized in a form matched to the channel transmission, for indication of the channel quality. Furthermore, the evaluation can be extended by taking account of a weighting function which allows assessment of application-specific requirements.

PRIORITY

[0001] This application claims priority to German application no. 103 18830.4 filed Apr. 25, 2003.

TECHNICAL FIELD OF THE INVENTION

[0002] The present invention relates to a method for evaluation of a biterror rate measurement for indication of a variable which ischaracteristic of the quality of a telecommunications connection.

DESCRIPTION OF RELATED ART AND BACKGROUND OF THE INVENTION

[0003] The channel quality of an active connection in telecommunicationssystems is typically checked continuously in order to make it possibleto take suitable measures if the quality of the connection is poor. Ifthe determined value for the channel quality is below a predeterminedtarget value, then, for example, the transmission power is increased inorder to increase the signal-to-noise power ratio.

[0004] Bit error rate measurements may be carried out in atelecommunications system in order to determine the channel quality of aconnection—which is represented by the quality index z. In this case,measurement values in the range between 0 and 1 are generally possible,by virtue of the definition of the bit error rate b as the ratio ofincorrectly received bits to the total number of received bits. Inpractice, the results are typically in a range I_(b) between 0 and 0.5.In contrast to this, the quality index z is stated as an integer value.For a Bluetooth radio interface, this is in the interval I_(z) of[0.255].

[0005] A linear mapping rule, which changes a measurement value of thebit error rate b from the value range I_(b) to a quality index z fromthe value range I_(z) does not result in efficient association. If, forexample, the value range I_(b) for a Bluetooth radio interface issubdivided into 256 identical individual intervals, which are thenassociated with the values z=255 to z=0, this leads in typicaltransmission scenarios to only a small number of z values or even only asingle z value occurring in the entire value range I_(z) of the qualityindex. The possible value range I_(z) is thus not used efficiently, anda rule based on a quality index such as this operates inefficiently.

[0006] One further problem is to take account of the relevant bit errorrate range in a worthwhile manner. This is because the entire bit errorrate range is typically not of the same interest. Frequently, moreaccurate allocation with higher quantization to the quality index isworthwhile in the relevant bit error rate range than in less relevantranges. The relevance of a bit error rate range is in this case governedby application-specific requirements.

SUMMARY OF THE INVENTION

[0007] The invention is based on the object of specifying a method bymeans of which—based on a bit error rate measurement—a variable which ischaracteristic of the quality of a telecommunications connection can bedetermined, which is particularly suitable for subsequent control andregulation purposes.

[0008] The objective on which the invention is based can be achieved bya method for evaluation of a bit error measurement for indication of avariable which is characteristic of the quality of a telecommunicationsconnection, comprising the steps of:

[0009] a) measuring the bit error rate, with measurement values whichare in a first value range being obtained; and

[0010] b) mapping the measurement values on the basis of a mapping ruleonto a second measurement range, with the characteristic variable beingobtained, and with the mapping rule taking account of a probabilitydistribution of the bit error rate.

[0011] The mapping rule also may take account of a weighting function,to which the probability distribution is linked, with a weightedprobability distribution being obtained. The linking process can becarried out by multiplication of a probability density function by theweighting function. The mapping rule can be determined solely by theprobability distribution, in particular by the probability densityfunction, and the weighting function. The mapping rule can be defined asfollows:

[0012] c1) determining n individual intervals and the associatedinterval boundaries from the first value range by means of an intervaldetermination rule using a probability density function of theprobability distribution, and

[0013] c2) allocating these intervals from the first value range to thesecond value range.

[0014] The interval boundaries x₁ and x_(i+1) of the i-th interval foreach of the total of n intervals can be determined on the basis of theinterval determination rule by equating the specific integral from x_(i)to x_(i+1) over the probability density function or the weightedprobability density function to a normalization variable. Thenormalization variable may correspond to 1/n. The second value range maycorrespond to a discrete value amount which, in particular, has 256digits. A number of mapping rules may be available. The number ofmapping rules can each be based on different probability distributions,in particular on different probability density functions, and/or ondifferent weighting functions. One or more mapping rules which have beencalculated in advance can be used. The method may further comprise thestep, which is carried out after step a) and before step b), ofselecting a suitable mapping function as a function of the transmissionscenario and/or of an application-specific requirement. Thecharacteristic variable can be used for controlling the channel quality,in particular for controlling the channel quality by variation of theemission power. The method can be used in a wireless Bluetoothtelecommunications system. The mapping rule can be defined as follows:

[0015] c1) determining n individual intervals and the associatedinterval boundaries from the first value range by means of an intervaldetermination rule using a weighted probability density function of theweighted probability density distribution; and

[0016] c2) allocating these intervals from the first value range to thesecond value range.

[0017] One major idea of the invention is to take account not only ofthe measured bit error rate but also of the transmission characteristicsand, in the process, their influence on the probability of occurrence ofthe bit error rate values, with the aid of the probability distributionof the bit error rate, when determining the channel quality of thetelecommunications connection using the quality index z as thecharacteristic variable. This results in the value range I_(b) beingquantized in a manner which is matched to the transmission channel andthus to the characteristic of the bit error rate. In consequence, finequantization is possible into bit error rate ranges with a highprobability of occurrence, while bit error rates which occur less oftenare quantized more coarsely.

[0018] The mapping rule on which this is based and which changes themeasured bit error rate to the quality index z taking account of theprobability distribution, is extended by this rule also taking accountof a weighting function which is linked to the probability distribution.The use of a weighting function makes it possible to achieve a relevanceassessment, to be precise in such a way that the fine quantization ofthe bit error rate is moved to areas of higher relevance. In acorresponding manner, the relevant bit error rate ranges are mappedparticularly accurately onto the quality index z, and less relevantranges of the bit error rate are mapped less accurately onto the qualityindex z. For example, the use of the weighted function allows higherprotocol layers in the telecommunications system to assessapplication-specific requirements. Furthermore, the influence of aquality control algorithm on the probability of occurrence of the biterror rate can be assessed with the assistance of the weighted function.Consideration of the weighting function thus allows an additional degreeof freedom for a relevance assessment, which allows optimum calculationof the variable that is characteristic of the channel quality, matchedto the respective application.

[0019] The mapping rule is preferably determined completely on the basisof the two variables comprising the probability distribution, inparticular the probability density function, and the weighting function.There is no need for any further influencing variables in this case.

[0020] One preferred embodiment of the invention provides for thecharacterization of the connection to be extended to two or more suchmapping rules, in which case, in particular, the different mapping rulesmay be based on different probability density functions and/or differentweighting functions. In consequence, differently optimized maps may becalculated and used either for different transmission scenarios or fordifferent application-specific requirements, so that an optimizedresponse can be achieved in widely differing conditions.

[0021] A further preferred embodiment is characterized in that themapping rules are calculated in advance and are stored in a memorydevice, so that no calculations are required during operation. This onthe one hand reduces the computational complexity and the hardwarecomplexity associated with it, and on the other hand reduces theperformance loss for carrying out the method. In particular, this allowsmapping functions which have been calculated in advance to beimplemented during manufacture, and these mapping functions can beselected on a scenario-specific and/or application-specific basis duringsubsequent use.

[0022] One preferred refinement of the invention allows the channelquality to be controlled with the aid of the characteristic variablewhich is calculated according to the invention, in particular allowingthe channel quality to be controlled by variation of the emission power.This means, inter alia, that it is possible to set the operating pointof the transmitter as a function of the application-specificrequirements, such that it operates with optimum loss of performance.

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The invention will be explained in more detail in the followingtext using an exemplary embodiment and with reference to the drawings,in which:

[0024]FIG. 1A shows an illustration of the profile of the probabilitydensity function pb(x) for an example of a transmission channel, plottedagainst the bit error rate;

[0025]FIG. 1B shows an illustration of the profile of the weightingfunction g(x) plotted against the bit error rate for an example of anapplication-specific relevance assessment;

[0026]FIG. 2A shows an illustration of the profile, which results fromFIG. 1A and FIG. 1B, of the weighted probability density function on alinear scale, with 7 examples of interval boundaries x₃₂, x₆₄, x₉₆,x₁₂₈, x₁₆₀, x₁₉₂ and x₂₂₄;

[0027]FIG. 2B shows an illustration of the profile, which results fromFIG. 1A and FIG. 1B, of the weighted probability density function on alogarithmic scale with 7 examples of interval boundaries x₃₂, x₆₄, x₉₆,x₁₂₈, x₁₆₀, x₁₉₂ and x₂₂₄; and

[0028]FIG. 3 shows an illustration of the map of the bit error rate bonto the quality index z, with quantization of the bit error rate b andan example of a map of bit error rate values from 8 intervals with theinterval boundaries x₃₂, x₆₄, x₉₆, x₁₂₈, x₁₆₀, x₁₉₂ and x₂₂₄ to theassociated z values z=32, z=64, z=96, z=128, z=160, z=192 and z=224.

DETAILED DESCRIPTION OF EMBODIMENTS

[0029] In the exemplary embodiment of the invention, which will beexplained with reference to the figures, the process of mapping the biterror rate b onto the control index z can be structured on the basis offour steps.

[0030] 1. In a first step, the probability density function p _(b)(x) ofthe bit error rate is determined. This may be done analytically,numerically by simulation or by calling values which have been storedappropriately in advance in a memory device. The profile, illustrated inFIG. 1A, of a probability density function, which is shown by way ofexample but is characteristic of the practical situation, of the biterror rate with a linear x-axis scale shows that a significantoccurrence of bit errors can be seen only for narrow ranges of thepossible total range of the bit error rate from 0 to 1. In the presentcase, the maximum of the probability density function p_(b)(x) and thusthe point of the maximum bit error rate probability occurs at a biterror rate b of approximately 0.025, and flattens out very quickly forvalues greater than this value.

[0031] 2. In addition, a weighting function g(x) may be defined in asecond step, and this is multiplied by p_(b)(x). The resultant productof the weighted probability density function of the bit error ratep′_(b)(x)=g(x)·p_(b)(x) should sensibly be normalized on the basis of∫_(x = 0)^(x = 1)p_(b)^(′)(x)  x = 1.

[0032] The function g(x) in this case takes account of particularlyrelevant bit error ranges by greater weighting in comparison to lessstrongly weighted, irrelevant ranges. The weighted probability densityfunction p′_(b)(x) obtained in this way thus includes both thesystem-inherent transmission characteristics by virtue of theprobability density function p_(b)(x) and the application-specificrequirements in the form of g(x). If the application-specificrequirements are ignored, g(x) should be chosen to be equal to unity.

[0033] The profile of the weighted function g(x) illustrated in FIG. 1Bwith a linear x-axis scale shows that bit error rates in specificranges—in this case in the range from 10⁻³ to 10⁻²—can be stressed to agreater extent for an application-specific relevance assessment.

[0034] 3. In a third step, the value range of the bit error rate is nowsubdivided by means of the weighted probability density functionp′_(b)(x) into a specific number n of intervals, such that, for all ofthe interval boundaries x_(i) where i=0..n−1:${\int_{x = x_{i}}^{x = x_{i + 1}}{{p_{b}^{\prime}(x)}\quad {x}}} = {\frac{1}{n}.}$

[0035] For the special case of a Bluetooth radio connection withpossible values of the quality index z from 0 to 256, this results, inparticular for the interval boundaries x_(i)—that is to say for x₀ tox₂₅₅ in:${\int_{x = x_{i}}^{x = x_{i + 1}}{{p_{b}^{\prime}(x)}\quad {x}}} = {\frac{1}{256}.}$

[0036] Normalization of each interval to a normalization variable suchas 1/n or 1/256 in the case of a Bluetooth radio connection allows theprobability of occurrence for each of the intervals I_(i)=[x_(i),x_(i+1)) to be the same. FIG. 2A and FIG. 2B show the profile of anexample of a weighted probability density function p′_(b)(x) on a linearscale and a logarithmic scale, respectively, corresponding to theprofiles of the functions in FIG. 1A and FIG. 2B, respectively. In thiscase, it can be seen that the weighted probability density has a maximumin the relevant range between 10⁻³ and 10⁻2. In addition, forillustration reasons, only 7 interval boundaries x₃₂, x₆₄, x₉₆, x₁₂₈,x₁₆₀, x₁₉₂ and x₂₂₄ of the total of 256 interval boundaries required fora Bluetooth radio connection are shown, subdividing the value range ofthe bit error rate I_(b) into 8 intervals, corresponding to step 3.

[0037] 4. In a fourth step, the map A is then defined for a number n ofintervals by the following association: $z = \left\{ \begin{matrix}{{n - {1\quad {for}\quad b}} \in \left\lbrack {x_{0},x_{1}} \right\rbrack} \\{{n - {2\quad {for}\quad b}} \in \left\lbrack {x_{1},x_{2}} \right\rbrack} \\\vdots \\{{0\quad {for}\quad b} \in \left\lbrack {x_{n - 2},x_{n - 1}} \right\rbrack}\end{matrix} \right.$

[0038] In the special case of a Bluetooth radio connection with 256intervals, the map is defined by the following special association:$z = \left\{ \begin{matrix}{{255\quad {for}\quad b} \in \left\lbrack {x_{0},x_{1}} \right\rbrack} \\{{254\quad {for}\quad b} \in \left\lbrack {x_{1},x_{2}} \right\rbrack} \\\vdots \\{{0\quad {for}\quad b} \in \left\lbrack {x_{n - 2},x_{n - 1}} \right\rbrack}\end{matrix} \right.$

[0039] The map which is obtained by the described method is illustratedin FIG. 3 for the example of a weighted probability density function andfor the total of only 7 illustrated interval boundaries from FIG. 2A orFIG. 2B. The illustration shows how measured bit error rate values from8 intervals with the corresponding interval boundaries x₃₂, x₆₄, x₉₆,x₁₂₈, x₁₆₀, x₁₉₂ and x₂₂₄ can be mapped onto the associated z valuesz=32, z=64, z=96, z=128, z=160, z=192 and z=224. This shows that,according to the invention, finer quantization is achieved in thesignificant or relevant bit error rate ranges which are determined bythe probability density function and the weighted function than in theless significant and less relevant higher or lower bit error rateranges.

[0040] The described method for determination of an efficient mapbetween the bit error rate and the quality index z allows the use ofefficient quality control methods by the host or host controller for atelecommunications connection. The efficiency is achieved by takingaccount of transmission characteristics and application-specificrequirements. If the invention is used for a Bluetooth radio connection,it is now possible for the host to use a specific commandHCI_Get_Link_Quality command, which it sends to the so-called hostcontroller, to check the quality of the connection, also referred to asthe link quality, of the connection that is active at that time. Inresponse, the host controller has to send back a value from 0 to 255—thesaid quality index—which reflects the quality of the connection. Thismeans that, the higher the value, the better is the quality of theconnection as well, and the lower is the bit error rate. The value ofthe quality index determined in this way can then typically be used forquality control: if the determined value of the quality index issomewhat below a predetermined target value, then the transmission power(and thus the signal-to-noise power ratio (SINR) and the quality index)can in consequence be increased, and vice versa. Furthermore, otherparameters which influence the channel quality, such as the coding,modulation type or data rate that are used, may also be regulated orcontrolled as a function of the quality index.

We claim:
 1. A method for evaluation of a bit error measurement for indication of a variable which is characteristic of the quality of a telecommunications connection, comprising the steps of: a) measuring the bit error rate, with measurement values which are in a first value range being obtained; and b) mapping the measurement values on the basis of a mapping rule onto a second measurement range, with the characteristic variable being obtained, and with the mapping rule taking account of a probability distribution of the bit error rate.
 2. The method according to claim 1, wherein the mapping rule also takes account of a weighting function, to which the probability distribution is linked, with a weighted probability distribution being obtained.
 3. The method according to claim 2, wherein the linking process is carried out by multiplication of a probability density function by the weighting function.
 4. The method according to claim 2, wherein the mapping rule is determined solely by the probability distribution, in particular by the probability density function, and the weighting function.
 5. The method according to claim 1, wherein the mapping rule is defined as follows: c1) determining n individual intervals and the associated interval boundaries from the first value range by means of an interval determination rule using a probability density function of the probability distribution, and c2) allocating these intervals from the first value range to the second value range.
 6. The method according to claim 5, wherein the interval boundaries x₁ and x_(i+1) of the i-th interval for each of the total of n intervals are determined on the basis of the interval determination rule by equating the specific integral from x_(i) to x_(i+1) over the probability density function to a normalization variable.
 7. The method according to claim 6, wherein the normalization variable corresponds to 1/n.
 8. The method according to claim 6, wherein the second value range corresponds to a discrete value amount which, in particular, has 256 digits.
 9. The method according to claim 2, wherein a number of mapping rules are available.
 10. The method according to claim 9, wherein the number of mapping rules are each based on different probability distributions, in particular on different probability density functions, and/or on different weighting functions.
 11. The method according to claim 1, wherein one or more mapping rules which have been calculated in advance are used.
 12. The method according to claim 9, comprising the step, which is carried out after step a) and before step b), of: selecting a suitable mapping function as a function of the transmission scenario and/or of an application-specific requirement.
 13. The method according to claim 2, wherein the characteristic variable is used for controlling the channel quality, in particular for controlling the channel quality by variation of the emission power.
 14. The method according to claim 1, wherein the method is used in a wireless Bluetooth telecommunications system.
 15. The method according to claim 2, wherein the mapping rule is defined as follows: c1) determining n individual intervals and the associated interval boundaries from the first value range by means of an interval determination rule using a weighted probability density function of the weighted probability density distribution; and c2) allocating these intervals from the first value range to the second value range.
 16. The method according to claim 15, wherein the interval boundaries x_(i) and x_(i+1) of the i-th interval for each of the total of n intervals are determined on the basis of the interval determination rule by equating the specific integral from x_(i) to x_(i+1) over the weighted probability density function to a normalization variable.
 17. The method according to claim 16, wherein the normalization variable corresponds to 1/n.
 18. The method according to claim 16, wherein the second value range corresponds to a discrete value amount which, in particular, has 256 digits. 